Zhen Lei
Biographic Data
| ID | 3725613 |
|---|---|
| NAME | Zhen Lei |
| GIVEN NAMES | Zhen |
| FAMILY NAME | Lei |
| SIGNATURE | LEI Z |
| AFFILIATIONS | Wuhan University of Technology |
| ORCID | 0000-0002-0826-9763 |
| VERIFIED | Yes |
| TOTAL WORKS | 17 |
| TOTAL CITATIONS | 19 |
| WORKS AS AUTHOR | 17 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Mechanically Proving Complex Properties of Integer Linear Programs
Integer Linear Programming (ILP) is a powerful way to formulate sophisticated optimization models for making geospatial decisions in GIS. One of the general modeling constructs in ILP is the multi-level closest assignment (MLCA) constraint in the reliable facility location models with facility failure considerations. Compared with simpler constructs (such as the single-level closest assignment constraint), it involves assigning customers to backu…
Motivated by karma
Massively Parallel Lagrangian Relaxation Algorithm for Solving Large-Scale Spatial Optimization Problems Using GPGPU
Lagrangian Relaxation (LR) is an effective method for solving spatial optimization problems in geospatial analysis and GIS. Among others, it has been used to solve the classic p-median problem that served as a unified local model in GIS since the 1990s. Despite its efficiency, the LR algorithm has seen limited usage in practice and is not as widely used as off-the-shelf solvers such as OPL/CPLEX or GPLK. This is primarily because of the high cost…
Solving Spatial Optimization Problems via Lagrangian Relaxation and Automatic Gradient Computation
Spatial optimization is an integral part of GIS and spatial analysis. It involves making various decisions in space, ranging from the location of public facilities to vehicle routing and political districting. While useful, such problems (especially large problem instances) are often difficult to solve using general mathematical programming (due to their generality). Traditionally, an alternative solution method is Lagrangian relaxation, which, i…
Computerized Proof of Fundamental Properties of the p-Median Problem Using Integer Linear Programming and a Theorem Prover
The p-median problem is one of the earliest location-allocation models used in spatial analysis and GIS. It involves locating a set of central facilities (the location decision) and allocating customers to these facilities (the allocation decision) so as to minimize the total transportation cost. It is important not only because of its wide use in spatial analysis but also because of its role as a unifying location model in GIS. A classical way o…
On the Theoretical Link between Optimized Geospatial Conflation Models for Linear Features
Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem (conceptualized in the 1980s) is one of the earliest conflation methods, in which GIS features from two maps are matched by minimizing their total discrepancy or dist…
Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model
Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the …
Large-scale integration of remotely sensed and GIS road networks
Road networks play an important role in the sustainable development of human society. Conventionally, there are two sources of road data acquisition: road extraction from Remote Sensing (RS) imagery and GIS based map production. Each method has its limitations. The RS road extraction methods are primarily raster-based and the extracted roads are not directly usable in GIS due to their fragmented and noisy nature, while vector-based methods cannot…
Ex ante litigation risk and firm restatement decisions
This study examines whether ex ante securities litigation risk prompts firms to make more or less voluntary restatements. The litigation risk is captured by a new measure based on the dismissal rate of the district court where the firm is headquartered. We find that misreporting firms headquartered in lenient (high dismissal rate) court jurisdictions are more likely to make voluntary restatements. Using the U.S. Supreme Court’s Tellabs decision a…
Intention and upstream indirect reciprocity
Towards Topological Geospatial Conflation
Geospatial data conflation is the process of identifying and merging the corresponding features in two datasets that represent the same objects in reality. Conflation is needed in a wide range of geospatial analyses, yet it is a difficult task, often considered too unreliable and costly due to various discrepancies between GIS data sources. This study addresses the reliability issue of computerized conflation by developing stronger optimization-b…
Mining, loss, and despair
Harmonizing Full and Partial Matching in Geospatial Conflation
Spatial data conflation is aimed at matching and merging objects in two datasets into a more comprehensive one. Starting from the “map assignment problem” in the 1980s, optimized conflation models treat feature matching as a natural optimization problem of minimizing certain metrics, such as the total discrepancy. One complication in optimized conflation is that heterogeneous datasets can represent geographic features differently. Features can co…
The brain mechanism of explicit and implicit processing of emotional prosodies
摘要
Multi-sector reduction potential of embodied carbon emissions in China
Understanding the Evolution of Sustainable Consumption Research
In recent years, the problem triad of population, environment and development has been joined by the added element of consumption, with a consequent boom in studies on sustainable consumption. This study provides a holistic and quantitative overview of these studies, aiming to track the evolution of research on sustainable consumption, and identify the key authors and co‐authorship network. Using the bibliometric method and network analysis, this…
Face Recognition Models
Understanding the Evolution of Sustainable Consumption Research
In recent years, the problem triad of population, environment and development has been joined by the added element of consumption, with a consequent boom in studies on sustainable consumption. This study provides a holistic and quantitative overview of these studies, aiming to track the evolution of research on sustainable consumption, and identify the key authors and co‐authorship network. Using the bibliometric method and network analysis, this…
Mining, loss, and despair
Face Recognition Models
Understanding the Evolution of Sustainable Consumption Research
In recent years, the problem triad of population, environment and development has been joined by the added element of consumption, with a consequent boom in studies on sustainable consumption. This study provides a holistic and quantitative overview of these studies, aiming to track the evolution of research on sustainable consumption, and identify the key authors and co‐authorship network. Using the bibliometric method and network analysis, this…
Multi-sector reduction potential of embodied carbon emissions in China
The brain mechanism of explicit and implicit processing of emotional prosodies
摘要
Harmonizing Full and Partial Matching in Geospatial Conflation
Spatial data conflation is aimed at matching and merging objects in two datasets into a more comprehensive one. Starting from the “map assignment problem” in the 1980s, optimized conflation models treat feature matching as a natural optimization problem of minimizing certain metrics, such as the total discrepancy. One complication in optimized conflation is that heterogeneous datasets can represent geographic features differently. Features can co…
Towards Topological Geospatial Conflation
Geospatial data conflation is the process of identifying and merging the corresponding features in two datasets that represent the same objects in reality. Conflation is needed in a wide range of geospatial analyses, yet it is a difficult task, often considered too unreliable and costly due to various discrepancies between GIS data sources. This study addresses the reliability issue of computerized conflation by developing stronger optimization-b…
Mining, loss, and despair
On the Theoretical Link between Optimized Geospatial Conflation Models for Linear Features
Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem (conceptualized in the 1980s) is one of the earliest conflation methods, in which GIS features from two maps are matched by minimizing their total discrepancy or dist…
Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model
Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the …
Large-scale integration of remotely sensed and GIS road networks
Road networks play an important role in the sustainable development of human society. Conventionally, there are two sources of road data acquisition: road extraction from Remote Sensing (RS) imagery and GIS based map production. Each method has its limitations. The RS road extraction methods are primarily raster-based and the extracted roads are not directly usable in GIS due to their fragmented and noisy nature, while vector-based methods cannot…
Ex ante litigation risk and firm restatement decisions
This study examines whether ex ante securities litigation risk prompts firms to make more or less voluntary restatements. The litigation risk is captured by a new measure based on the dismissal rate of the district court where the firm is headquartered. We find that misreporting firms headquartered in lenient (high dismissal rate) court jurisdictions are more likely to make voluntary restatements. Using the U.S. Supreme Court’s Tellabs decision a…
Intention and upstream indirect reciprocity
Massively Parallel Lagrangian Relaxation Algorithm for Solving Large-Scale Spatial Optimization Problems Using GPGPU
Lagrangian Relaxation (LR) is an effective method for solving spatial optimization problems in geospatial analysis and GIS. Among others, it has been used to solve the classic p-median problem that served as a unified local model in GIS since the 1990s. Despite its efficiency, the LR algorithm has seen limited usage in practice and is not as widely used as off-the-shelf solvers such as OPL/CPLEX or GPLK. This is primarily because of the high cost…
Solving Spatial Optimization Problems via Lagrangian Relaxation and Automatic Gradient Computation
Spatial optimization is an integral part of GIS and spatial analysis. It involves making various decisions in space, ranging from the location of public facilities to vehicle routing and political districting. While useful, such problems (especially large problem instances) are often difficult to solve using general mathematical programming (due to their generality). Traditionally, an alternative solution method is Lagrangian relaxation, which, i…
Computerized Proof of Fundamental Properties of the p-Median Problem Using Integer Linear Programming and a Theorem Prover
The p-median problem is one of the earliest location-allocation models used in spatial analysis and GIS. It involves locating a set of central facilities (the location decision) and allocating customers to these facilities (the allocation decision) so as to minimize the total transportation cost. It is important not only because of its wide use in spatial analysis but also because of its role as a unifying location model in GIS. A classical way o…
Mechanically Proving Complex Properties of Integer Linear Programs
Integer Linear Programming (ILP) is a powerful way to formulate sophisticated optimization models for making geospatial decisions in GIS. One of the general modeling constructs in ILP is the multi-level closest assignment (MLCA) constraint in the reliable facility location models with facility failure considerations. Compared with simpler constructs (such as the single-level closest assignment constraint), it involves assigning customers to backu…
Motivated by karma
Computer Science (10 works) · Geography (7 works) · Mathematics (6 works) · Cartography (5 works) · Artificial Intelligence (4 works) · Conflation (4 works) · Data mining (4 works) · Economics (4 works) · Engineering (4 works) · Facility Location and Emergency Management (4 works)